Epoch: 0001 train_loss= 0.70118 train_acc= 0.51212 val_loss= 0.69819 val_acc= 0.57377 time= 0.12502
Epoch: 0002 train_loss= 0.69816 train_acc= 0.57576 val_loss= 0.69608 val_acc= 0.59016 time= 0.01562
Epoch: 0003 train_loss= 0.69598 train_acc= 0.57273 val_loss= 0.69467 val_acc= 0.75410 time= 0.00000
Epoch: 0004 train_loss= 0.69457 train_acc= 0.66364 val_loss= 0.69383 val_acc= 0.63934 time= 0.01563
Epoch: 0005 train_loss= 0.69375 train_acc= 0.63333 val_loss= 0.69339 val_acc= 0.65574 time= 0.01563
Epoch: 0006 train_loss= 0.69302 train_acc= 0.66970 val_loss= 0.69322 val_acc= 0.63934 time= 0.00000
Epoch: 0007 train_loss= 0.69259 train_acc= 0.61818 val_loss= 0.69327 val_acc= 0.60656 time= 0.01563
Epoch: 0008 train_loss= 0.69255 train_acc= 0.57576 val_loss= 0.69345 val_acc= 0.55738 time= 0.01563
Epoch: 0009 train_loss= 0.69273 train_acc= 0.61212 val_loss= 0.69362 val_acc= 0.52459 time= 0.00000
Epoch: 0010 train_loss= 0.69242 train_acc= 0.65758 val_loss= 0.69370 val_acc= 0.55738 time= 0.01563
Epoch: 0011 train_loss= 0.69262 train_acc= 0.64242 val_loss= 0.69372 val_acc= 0.57377 time= 0.01563
Epoch: 0012 train_loss= 0.69244 train_acc= 0.55455 val_loss= 0.69356 val_acc= 0.63934 time= 0.00000
Epoch: 0013 train_loss= 0.69177 train_acc= 0.69697 val_loss= 0.69338 val_acc= 0.63934 time= 0.01563
Epoch: 0014 train_loss= 0.69207 train_acc= 0.63030 val_loss= 0.69309 val_acc= 0.62295 time= 0.01563
Epoch: 0015 train_loss= 0.69127 train_acc= 0.65758 val_loss= 0.69277 val_acc= 0.65574 time= 0.00000
Epoch: 0016 train_loss= 0.69098 train_acc= 0.66667 val_loss= 0.69252 val_acc= 0.70492 time= 0.01563
Epoch: 0017 train_loss= 0.69141 train_acc= 0.66364 val_loss= 0.69226 val_acc= 0.73770 time= 0.01563
Epoch: 0018 train_loss= 0.69079 train_acc= 0.63333 val_loss= 0.69227 val_acc= 0.63934 time= 0.00000
Epoch: 0019 train_loss= 0.69052 train_acc= 0.66667 val_loss= 0.69232 val_acc= 0.62295 time= 0.01563
Epoch: 0020 train_loss= 0.69017 train_acc= 0.61818 val_loss= 0.69227 val_acc= 0.63934 time= 0.00000
Epoch: 0021 train_loss= 0.68968 train_acc= 0.65455 val_loss= 0.69213 val_acc= 0.62295 time= 0.00000
Epoch: 0022 train_loss= 0.68913 train_acc= 0.69697 val_loss= 0.69200 val_acc= 0.62295 time= 0.01562
Epoch: 0023 train_loss= 0.68927 train_acc= 0.68182 val_loss= 0.69182 val_acc= 0.62295 time= 0.00000
Epoch: 0024 train_loss= 0.68870 train_acc= 0.64242 val_loss= 0.69159 val_acc= 0.62295 time= 0.01563
Epoch: 0025 train_loss= 0.68932 train_acc= 0.63636 val_loss= 0.69165 val_acc= 0.63934 time= 0.01563
Epoch: 0026 train_loss= 0.68911 train_acc= 0.64545 val_loss= 0.69153 val_acc= 0.63934 time= 0.00000
Epoch: 0027 train_loss= 0.68731 train_acc= 0.64848 val_loss= 0.69124 val_acc= 0.65574 time= 0.01563
Epoch: 0028 train_loss= 0.68716 train_acc= 0.68788 val_loss= 0.69100 val_acc= 0.75410 time= 0.01563
Epoch: 0029 train_loss= 0.68866 train_acc= 0.71212 val_loss= 0.69087 val_acc= 0.75410 time= 0.00000
Epoch: 0030 train_loss= 0.68709 train_acc= 0.67879 val_loss= 0.69070 val_acc= 0.75410 time= 0.01563
Epoch: 0031 train_loss= 0.68799 train_acc= 0.66970 val_loss= 0.69066 val_acc= 0.70492 time= 0.01563
Epoch: 0032 train_loss= 0.68730 train_acc= 0.67879 val_loss= 0.69058 val_acc= 0.65574 time= 0.00000
Epoch: 0033 train_loss= 0.68681 train_acc= 0.66061 val_loss= 0.69043 val_acc= 0.67213 time= 0.01563
Epoch: 0034 train_loss= 0.68647 train_acc= 0.63636 val_loss= 0.68973 val_acc= 0.73770 time= 0.01563
Epoch: 0035 train_loss= 0.68559 train_acc= 0.65758 val_loss= 0.68957 val_acc= 0.73770 time= 0.00000
Epoch: 0036 train_loss= 0.68671 train_acc= 0.67879 val_loss= 0.68972 val_acc= 0.73770 time= 0.01563
Epoch: 0037 train_loss= 0.68625 train_acc= 0.66364 val_loss= 0.68972 val_acc= 0.75410 time= 0.01563
Epoch: 0038 train_loss= 0.68749 train_acc= 0.64545 val_loss= 0.69016 val_acc= 0.63934 time= 0.00000
Epoch: 0039 train_loss= 0.68419 train_acc= 0.70606 val_loss= 0.69123 val_acc= 0.62295 time= 0.01563
Early stopping...
Optimization Finished!
Test set results: cost= 0.69129 accuracy= 0.68033 time= 0.00000 
